Kleber M. Silva

Bio: Kleber M. Silva is an academic researcher from University of Brasília. The author has contributed to research in topics: Fault (power engineering) & Wavelet. The author has an hindex of 15, co-authored 72 publications receiving 994 citations. Previous affiliations of Kleber M. Silva include Federal University of Campina Grande.

Fault detection and classification in transmission lines based on wavelet transform and ANN

Abstract: This paper proposes a novel method for transmission-line fault detection and classification using oscillographic data. The fault detection and its clearing time are determined based on a set of rules obtained from the current waveform analysis in time and wavelet domains. The method is able to single out faults from other power-quality disturbances, such as voltage sags and oscillatory transients, which are common in power systems operation. An artificial neural network classifies the fault from the voltage and current waveforms pattern recognition in the time domain. The method has been used for fault detection and classification from real oscillographic data of a Brazilian utility company with excellent results

Accurate Two-Terminal Transmission Line Fault Location Using Traveling Waves

Abstract: This paper presents an innovative two-terminal traveling wave (TW)-based fault location formulation. It depends only on the time difference between the first incident TW and the successive reflection from the fault point, at both line ends. Thereby, the proposed formulation requires neither data synchronization nor line parameters, which are sources of error that usually affect TW-based fault location schemes. Several faults on a typical 500 kV line were simulated to compare the proposed formulation performance with that of a classical two-end approach. The obtained results attest that the proposed formulation is able to accurately locate faults on transmission lines, even when data synchronism errors and uncertainties in the monitored line parameters exist.

Real-Time Traveling-Wave-Based Fault Location Using Two-Terminal Unsynchronized Data

Abstract: In this paper, a new two-terminal traveling-wave-based fault-location algorithm is proposed. Its main advantage over similar two-terminal algorithms lies in the fact it does not require the data from both line terminals to be synchronized. In order to do so, the algorithm is applied in real time, and a communication system is used, whose data-transmission latency is taken into account in the proposed formulation. The fault locator routines were implemented using the real-time digital simulator (RTDS), such that a wide variety of fault scenarios in a 230-kV transmission line 200 km long was evaluated in real time, considering communication systems with different latency variability levels. The obtained results indicate the proposed algorithm is able to locate faults using either synchronized or unsynchronized two-terminal data, whereas classical methods work properly for synchronized measurements only.

Power Transformer Differential Protection Using the Boundary Discrete Wavelet Transform

Abstract: Currently, the differential function is the most used for the power transformer protection, leading to a reliable discrimination between internal faults and other events. However, the conventional phasor-based differential protection function presents difficulties in the detection of some internal faults and its performance depends on the harmonic restraint and blocking functions in order to avoid relay misoperation during inrush currents. However, internal faults and other disturbances present transient, which can be properly detected by using the wavelet transform. This paper recreates the phase current and the negative-sequence current differential elements by means of the boundary wavelet coefficient energy. The proposed method was evaluated by using representative simulations of internal faults, external faults, and transformer energizations in two different power systems. By using the boundary wavelet coefficient energy instead of phasor estimation, the proposed method was quite fast, accurate, was not affected by inrush currents in transformer energizations and fault clearance, and could be used in a real-time application with low computational burden. In addition, the proposed method presented no failure in fault with overdamped transients, was scarcely affected by the choice of the mother wavelet, presented no time delay associated with the wavelet filtering, and was not affected by typical noise.

Two-Terminal Traveling-Wave-Based Transmission-Line Protection

Abstract: This paper proposes a two-terminal traveling-wave-based transmission-line protection using only the first wavefront arrival time at each line terminal (polarity and magnitude of transients are not required), leading to a simple, accurate, and fast protection. In real time, the protection system detects internal and external faults and identifies the fault directionality. In addition, the protection zone and the protection operating time are also formulated. The effects of the sampling rate and the traveling-wave propagation velocity estimation are considered, and some paradigms, such as the need of a high-sampling frequency and an accurate traveling-wave propagation velocity estimation are properly addressed. Two traveling-wave-based protective relays were implemented and evaluated in realistic real-time hardware-in-the-loop simulations considering the communication equipment between the relays, in which reliability and protection operating time could be assessed accordingly.

Fault detection and classification in transmission lines based on wavelet transform and ANN

Abstract: This paper proposes a novel method for transmission-line fault detection and classification using oscillographic data. The fault detection and its clearing time are determined based on a set of rules obtained from the current waveform analysis in time and wavelet domains. The method is able to single out faults from other power-quality disturbances, such as voltage sags and oscillatory transients, which are common in power systems operation. An artificial neural network classifies the fault from the voltage and current waveforms pattern recognition in the time domain. The method has been used for fault detection and classification from real oscillographic data of a Brazilian utility company with excellent results

A critical review of detection and classification of power quality events

Abstract: Requirement of green supply with higher quality has been consumers’ demand around the globe The electrical power system is expected to deliver undistorted sinusoidal rated voltage and current continuously at rated frequency to the consumers This paper presents a comprehensive review of signal processing and intelligent techniques for automatic classification of the power quality (PQ) events and an effect of noise on detection and classification of disturbances It is intended to provide a wide spectrum on the status of detection and classification of PQ disturbances as well as an effect of noise on detection and classification of PQ events to the researchers, designers and engineers working on power quality More than 150 research publications on detection and classification techniques of PQ disturbances have been critically examined, classified and listed for quick reference

Fault detection, classification and location for transmission lines and distribution systems: a review on the methods

Abstract: A comprehensive review on the methods used for fault detection, classification and location in transmission lines and distribution systems is presented in this study. Though the three topics are highly correlated, the authors try to discuss them separately, so that one may have a more logical and comprehensive understanding of the concepts without getting confused. Great significance is also attached to the feature extraction process, without which the majority of the methods may not be implemented properly. Fault detection techniques are discussed on the basis of feature extraction. After the overall concepts and general ideas are presented, representative works as well as new progress in the techniques are covered and discussed in detail. One may find the content of this study helpful as a detailed literature review or a practical technical guidance.

Power System Analysis And Design

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Fault location and detection techniques in power distribution systems with distributed generation: A review

Abstract: Distribution systems are continuously exposed to fault occurrences due to various reasons, such as lightning strike, failure of power system components due to aging of equipment and human errors. These phenomena affect the system reliability and results in expensive repairs, lost of productivity and power loss to customers. Since fault is unpredictable, a fast fault location and isolation is required to minimize the impact of fault in distribution systems. Therefore, many methods have been developed since the past to locate and detect faults in distribution systems with distributed generation. The methods can be divided into two categories, conventional and artificial intelligence techniques. Conventional techniques include travelling wave method and impedance based method while artificial intelligence techniques include Artificial Neural Network (ANN), Support Vector Machine (SVM), Fuzzy Logic, Genetic Algorithm (GA) and matching approach. However, fault location using intelligent methods are challenging since they require training data for processing and are time consuming. In this paper, most of the techniques that have been developed since the past and commonly used to locate and detect faults in distribution systems with distributed generation are reviewed. Research works in fault location area, the working principles, advantages and disadvantages of past works related to each fault location technique are highlighted in this paper. Hence, from this review, the opportunities in fault location research area in power distribution system can be explored further.